OpenRaiser/Intern-Atlas huggingface dataset:
Hiding in plain sight: insights about health-care trends gained through open health data
Venue: arXiv cs.CY 2017
Year: 2017

The open data movement constitutes an approach to achieving accountability for government organizations, and is aligned with one of the sustainable development goals outlined by the United Nations. In the area of health care, government agencies at the Federal and State levels have released open health data consisting of de-identified patient outcomes, costs and ratings. We have applied big data analytics to understand patterns and trends in open health data. We envision the use of this data by concerned citizens to understand both national and local trends in health expenditures. We have built an open-source tool, BOAT (Big Data Open Source Analytics Tool, https://github.com/fdudatamining) to facilitate analytical exploration of open health data sets. We used BOAT to analyze data from the New York Statewide Planning and Research Cooperative System and determined that there has been a significant increase (40 percent) in the incidences of mental health issues amongst adolescents from 2009-2014. Using BOAT we analyzed costs for hip replacement surgery for 168,676 patients is in New York State, and showed that 88% of these patients had surgery costs of less than \$30,000. This figure provides a basis to understand the decision by The California Public Employees' Retirement System to cap hip replacement reimbursements at $30,000, resulting in significant savings. Our tool could enable researchers, hospitals, insurers and citizens to obtain an unbiased view on health-care expenditures, costs and emerging trends. Our tool is especially valuable in the current economic environment, where a significant amount of reporting is controlled by special interests groups and lobbies.
An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition
Venue: arXiv cs.CV 2017
Year: 2017

We present an integrated framework for simultaneous tracking, group detection and multi-level activity recognition in crowd videos. Instead of solving these problems independently and sequentially, we solve them together in a unified framework to utilize the strong correlation that exists among individual motion, groups, and activities. We explore the hierarchical structure hidden in the video that connects individuals over time to produce tracks, connects individuals to form groups and also connects groups together to form a crowd. We show that estimation of this hidden structure corresponds to track association and group detection. We estimate this hidden structure under a linear programming formulation. The obtained graphical representation is further explored to recognize the node values that corresponds to multi-level activity recognition. This problem is solved under a structured SVM framework. The results on publicly available dataset show very competitive performance at all levels of granularity with the state-of-the-art batch processing methods despite the proposed technique being an online (causal) one.
Prediction of Satisfied User Ratio for Compressed Video
Venue: arXiv cs.MM 2017
Year: 2017

A large-scale video quality dataset called the VideoSet has been constructed recently to measure human subjective experience of H.264 coded video in terms of the just-noticeable-difference (JND). It measures the first three JND points of 5-second video of resolution 1080p, 720p, 540p and 360p. Based on the VideoSet, we propose a method to predict the satisfied-user-ratio (SUR) curves using a machine learning framework. First, we partition a video clip into local spatial-temporal segments and evaluate the quality of each segment using the VMAF quality index. Then, we aggregate these local VMAF measures to derive a global one. Finally, the masking effect is incorporated and the support vector regression (SVR) is used to predict the SUR curves, from which the JND points can be derived. Experimental results are given to demonstrate the performance of the proposed SUR prediction method.
The Capacity of Private Computation
Venue: arXiv cs.IT 2017
Year: 2017

We introduce the problem of private computation, comprised of $N$ distributed and non-colluding servers, $K$ independent datasets, and a user who wants to compute a function of the datasets privately, i.e., without revealing which function he wants to compute, to any individual server. This private computation problem is a strict generalization of the private information retrieval (PIR) problem, obtained by expanding the PIR message set (which consists of only independent messages) to also include functions of those messages. The capacity of private computation, $C$, is defined as the maximum number of bits of the desired function that can be retrieved per bit of total download from all servers. We characterize the capacity of private computation, for $N$ servers and $K$ independent datasets that are replicated at each server, when the functions to be computed are arbitrary linear combinations of the datasets. Surprisingly, the capacity, $C=\left(1+1/N+\cdots+1/N^{K-1}\right)^{-1}$, matches the capacity of PIR with $N$ servers and $K$ messages. Thus, allowing arbitrary linear computations does not reduce the communication rate compared to pure dataset retrieval. The same insight is shown to hold even for arbitrary non-linear computations when the number of datasets $K\rightarrow\infty$.
Dependability in Edge Computing
Venue: arXiv cs.DC 2017
Year: 2017

Edge computing is the practice of placing computing resources at the edges of the Internet in close proximity to devices and information sources. This, much like a cache on a CPU, increases bandwidth and reduces latency for applications but at a potential cost of dependability and capacity. This is because these edge devices are often not as well maintained, dependable, powerful, or robust as centralized server-class cloud resources. This article explores dependability and deployment challenges in the field of edge computing, what aspects are solvable with today's technology, and what aspects call for new solutions. The first issue addressed is failures, both hard (crash, hang, etc.) and soft (performance-related), and real-time constraint violation. In this domain, edge computing bolsters real-time system capacity through reduced end-to-end latency. However, much like cache misses, overloaded or malfunctioning edge computers can drive latency beyond tolerable limits. Second, decentralized management and device tampering can lead to chain of trust and security or privacy violations. Authentication, access control, and distributed intrusion detection techniques have to be extended from current cloud deployments and need to be customized for the edge ecosystem. The third issue deals with handling multi-tenancy in the typically resource-constrained edge devices and the need for standardization to allow for interoperability across vendor products. We explore the key challenges in each of these three broad issues as they relate to dependability of edge computing and then hypothesize about promising avenues of work in this area.
Characterizing the structural diversity of complex networks across domains
Venue: arXiv cs.SI 2017
Year: 2017

The structure of complex networks has been of interest in many scientific and engineering disciplines over the decades. A number of studies in the field have been focused on finding the common properties among different kinds of networks such as heavy-tail degree distribution, small-worldness and modular structure and they have tried to establish a theory of structural universality in complex networks. However, there is no comprehensive study of network structure across a diverse set of domains in order to explain the structural diversity we observe in the real-world networks. In this paper, we study 986 real-world networks of diverse domains ranging from ecological food webs to online social networks along with 575 networks generated from four popular network models. Our study utilizes a number of machine learning techniques such as random forest and confusion matrix in order to show the relationships among network domains in terms of network structure. Our results indicate that there are some partitions of network categories in which networks are hard to distinguish based purely on network structure. We have found that these partitions of network categories tend to have similar underlying functions, constraints and/or generative mechanisms of networks even though networks in the same partition have different origins, e.g., biological processes, results of engineering by human being, etc. This suggests that the origin of a network, whether it's biological, technological or social, may not necessarily be a decisive factor of the formation of similar network structure. Our findings shed light on the possible direction along which we could uncover the hidden principles for the structural diversity of complex networks.
ChainerMN: Scalable Distributed Deep Learning Framework
Venue: arXiv cs.DC 2017
Year: 2017

One of the keys for deep learning to have made a breakthrough in various fields was to utilize high computing powers centering around GPUs. Enabling the use of further computing abilities by distributed processing is essential not only to make the deep learning bigger and faster but also to tackle unsolved challenges. We present the design, implementation, and evaluation of ChainerMN, the distributed deep learning framework we have developed. We demonstrate that ChainerMN can scale the learning process of the ResNet-50 model to the ImageNet dataset up to 128 GPUs with the parallel efficiency of 90%.
A Computer Vision System to Localize and Classify Wastes on the Streets
Venue: arXiv cs.CV 2017
Year: 2017

Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we present a fully automated computer vision application for littering quantification based on images taken from the streets and sidewalks. We have employed a deep learning based framework to localize and classify different types of wastes. Since there was no waste dataset available, we built our acquisition system mounted on a vehicle. Collected images containing different types of wastes. These images are then annotated for training and benchmarking the developed system. Our results on real case scenarios show accurate detection of littering on variant backgrounds.
SVSGAN: Singing Voice Separation via Generative Adversarial Network
Venue: arXiv cs.SD 2017
Year: 2017

Separating two sources from an audio mixture is an important task with many applications. It is a challenging problem since only one signal channel is available for analysis. In this paper, we propose a novel framework for singing voice separation using the generative adversarial network (GAN) with a time-frequency masking function. The mixture spectra is considered to be a distribution and is mapped to the clean spectra which is also considered a distribtution. The approximation of distributions between mixture spectra and clean spectra is performed during the adversarial training process. In contrast with current deep learning approaches for source separation, the parameters of the proposed framework are first initialized in a supervised setting and then optimized by the training procedure of GAN in an unsupervised setting. Experimental results on three datasets (MIR-1K, iKala and DSD100) show that performance can be improved by the proposed framework consisting of conventional networks.
An Energy-Aware WLAN Discovery Scheme for LTE HetNet
Venue: arXiv cs.NI 2017
Year: 2017

Recently, there has been significant interest in the integration and co-existence of Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) with other Radio Access Technologies, like IEEE 802.11 Wireless Local Area Networks (WLANs). Although, the inter-working of IEEE 802.11 WLANs with 3GPP LTE has indicated enhanced network performance in the context of capacity and load balancing, the WLAN discovery scheme implemented in most of the commercially available smartphones is very inefficient and results in high battery drainage. In this paper, we have proposed an energy efficient WLAN discovery scheme for 3GPP LTE and IEEE 802.11 WLAN inter-working scenario. User Equipment (UE), in the proposed scheme, uses 3GPP network assistance along with the results of past channel scans, to optimally select the next channels to scan. Further, we have also developed an algorithm to accurately estimate the UE's mobility state, using 3GPP network signal strength patterns. We have implemented various discovery schemes in Android framework, to evaluate the performance of our proposed scheme against other solutions in the literature. Since, Android does not support selective scanning mode, we have implemented modules in Android to enable selective scanning. Further, we have also used simulation studies and justified the results using power consumption modeling. The results from the field experiments and simulations have shown high power savings using the proposed scanning scheme without any discovery performance deterioration.
Energy Efficiency of Multi-user Multi-antenna Random Cellular Networks with Minimum Distance Constraints
Venue: arXiv cs.NI 2017
Year: 2017

Compared with conventional regular hexagonal cellular models, random cellular network models resemble real cellular networks much more closely. However, most studies of random cellular networks are based on the Poisson point process and do not take into account the fact that adjacent base stations (BSs) should be separated with a minimum distance to avoid strong interference among each other. In this paper, based on the hard core point process (HCPP), we propose a multi-user multi-antenna random cellular network model with the aforementioned minimum distance constraint for adjacent BSs. Taking into account the effects of small scale fading and shadowing, interference and capacity models are derived for the multi-user multi-antenna HCPP random cellular networks. Furthermore, a spectrum efficiency model as well as an energy efficiency model is presented, based on which, the maximum achievable energy efficiency of the considered multi-user multiantenna HCPP random cellular networks is investigated. Simulation results demonstrate that the energy efficiency of conventional Poison point process (PPP) cellular networks is underestimated when the minimum distance between adjacent BSs is ignored.
Extracting Syntactic Patterns from Databases
Venue: arXiv cs.DB 2017
Year: 2017

Many database columns contain string or numerical data that conforms to a pattern, such as phone numbers, dates, addresses, product identifiers, and employee ids. These patterns are useful in a number of data processing applications, including understanding what a specific field represents when field names are ambiguous, identifying outlier values, and finding similar fields across data sets. One way to express such patterns would be to learn regular expressions for each field in the database. Unfortunately, exist- ing techniques on regular expression learning are slow, taking hundreds of seconds for columns of just a few thousand values. In contrast, we develop XSystem, an efficient method to learn patterns over database columns in significantly less time. We show that these patterns can not only be built quickly, but are expressive enough to capture a number of key applications, including detecting outliers, measuring column similarity, and assigning semantic labels to columns (based on a library of regular expressions). We evaluate these applications with datasets that range from chemical databases (based on a collaboration with a pharmaceutical company), our university data warehouse, and open data from MassData.gov.
Cyber-Automotive Simulation and Evaluation Platform for Vehicular Value Added Services
Venue: arXiv cs.CY 2017
Year: 2017

An easily moving and safe transportation is an indicator in any country in the world of economic growth and well-being. For the past 100 years, innovation within the automotive sector has brought major technological advances leading to safer, cleaner and more affordable vehicles. But for the most time since the inception of the moving assembly line for vehicle production by Henry Ford, the changes have been incremental / evolutionary. Thanks to the new possibilities due to the IT / wireless revolution, the automotive industry appears to be on the cusp of revolutionary change with potential to dramatically reshape not just the competitive landscape but also the way we interact with vehicles, and indeed the future design of our roads and cities. Apart from connected personal mobility, vehicles are also envisioned to provide Value Added Services (VAS) such as autonomous driving via Vehicle-to-Vehicle (V2V) and Vehicle-to-infrastructure (V2I), electric load balancing via Vehicle-to-Grid (V2G) solutions, communication solutions using Visible Light Communications (VLC) etc. The development and evaluation of vehicular VAS requires a modular and scalable multidisciplinary simulation platform. In this paper we propose a novel simulation platform named Cyber-Automotive Simulation \& Evaluation Platform (CASEP). The purpose of CASEP is to evaluate and visualize the gains of various vehicular VAS with special emphasis on commercial vehicle VAS. The use cases are evaluated with respect to the mission-specific performance indicators, thereby providing usable metrics for optimization. The visualization platform is being developed using the UNITY 3D engine, thereby enabling intuitive interaction as in real physics-based games
DCN+: Mixed Objective and Deep Residual Coattention for Question Answering
Venue: arXiv cs.CL 2017
Year: 2017

Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate. We propose a mixed objective that combines cross entropy loss with self-critical policy learning. The objective uses rewards derived from word overlap to solve the misalignment between evaluation metric and optimization objective. In addition to the mixed objective, we improve dynamic coattention networks (DCN) with a deep residual coattention encoder that is inspired by recent work in deep self-attention and residual networks. Our proposals improve model performance across question types and input lengths, especially for long questions that requires the ability to capture long-term dependencies. On the Stanford Question Answering Dataset, our model achieves state-of-the-art results with 75.1% exact match accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy and 86.0% F1.
Erratum: Link prediction in drug-target interactions network using similarity indices
Venue: arXiv cs.AI 2017
Year: 2017

Background: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc., and do not perform satisfactorily when such information is unavailable. We propose a new, alternative method for DTI prediction that makes use of only network topology information attempting to solve this problem. Results: We compare our method for DTI prediction against the well-known RBM approach. We show that when applied to the MATADOR database, our approach based on node neighborhoods yield higher precision for high-ranking predictions than RBM when no information regarding DTI types is available. Conclusion: This demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions.
Outdoor to Indoor Penetration Loss at 28 GHz for Fixed Wireless Access
Venue: arXiv cs.IT 2017
Year: 2017

This paper present the results from a 28 GHz channel sounding campaign performed to investigate the effects of outdoor to indoor penetration on the wireless propagation channel characteristics for an urban microcell in a fixed wireless access scenario. The measurements are performed with a real-time channel sounder, which can measure path loss up to 169 dB, and equipped with phased array antennas that allows electrical beam steering for directionally resolved measurements in dynamic environments. Thanks to the short measurement time and the excellent phase stability of the system, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. For outdoor and indoor receiver locations, we compare path loss, delay spreads and angular spreads obtained for two different types of buildings.
Dynamic Double Directional Propagation Channel Measurements at 28 GHz
Venue: arXiv cs.IT 2017
Year: 2017

This paper presents results from the (to our knowledge) first dynamic double-directionally resolved measurement campaign at mm-wave frequencies for an outdoor microcellular scenario. The measurements are performed with USC's real-time channel sounder equipped with phased array antennas that can steer beams electrically in microseconds, allowing directional measurements in dynamic environments. Exploiting the phase coherency of the setup, the multi-path components can be tracked over time to investigate the temporal dependencies of the channel characteristics. We present results for time-varying path-loss, delay spread, mean angles and angular spreads observed at the transmitter (TX) and receiver (RX) in the presence of moving vehicles and pedestrians. Additionally, we investigate excess losses observed due to blockage by vehicles and compare the cases when TX and RX are using fixed beams or when they are capable of adjusting beam directions dynamically.
Privacy Preserving and Collusion Resistant Energy Sharing
Venue: arXiv cs.CR 2017
Year: 2017

Energy has been increasingly generated or collected by different entities on the power grid (e.g., universities, hospitals and householdes) via solar panels, wind turbines or local generators in the past decade. With local energy, such electricity consumers can be considered as "microgrids" which can simulataneously generate and consume energy. Some microgrids may have excessive energy that can be shared to other power consumers on the grid. To this end, all the entities have to share their local private information (e.g., their local demand, local supply and power quality data) to each other or a third-party to find and implement the optimal energy sharing solution. However, such process is constrained by privacy concerns raised by the microgrids. In this paper, we propose a privacy preserving scheme for all the microgrids which can securely implement their energy sharing against both semi-honest and colluding adversaries. The proposed approach includes two secure communication protocols that can ensure quantified privacy leakage and handle collusions.